AI-Enabled Chest X-Ray Detects Subclinical Diastolic Dysfunction in Diabesity and its Therapeutic Responses to GLP-1 or GLP-1/GIP Receptor Agonists

AI-Enabled Chest X-Ray Detects Subclinical Diastolic Dysfunction in Diabesity and its Therapeutic Responses to GLP-1 or GLP-1/GIP Receptor Agonists | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report AI-Enabled Chest X-Ray Detects Subclinical Diastolic Dysfunction in Diabesity and its Therapeutic Responses to GLP-1 or GLP-1/GIP Receptor Agonists Richard D. White, Mutlu Demirer, Barbaros Selnur Erdal, Grace Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9272120/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction The evaluation of diastolic function in diabesity patients undergoing incretin-based therapy could facilitate early detection and help prevent progression to heart failure. Methodology A group of 15 diabesity cases with chest x-ray examinations (< 1 month) before and after ≥ 12 months of incretin-based therapy (absent ventricular/valvular dysfunction) were identified. Standard diabesity descriptors and predictions of pulmonary venous hypertension (aka “pulmonary congestion”) [None; Stage 1: vascular distention/redistribution but minimal interstitial edema; or Stage ≥ 2: vascular congestion with ≥ mild interstitial or alveolar edema] by validated AI-enabled chest x-ray staging, representing mean left atrial pressure in diastolic dysfunction, were evaluated pre- and post-therapy. Results By weight loss, cases clustered into equal-sized subgroups: 1. Significant Responders (9–30% decreases); 2. Insignificant Responders (0–2% decreases); and 3. Non-Responders (2–12% increases). Regarding hemoglobin A1c changes: 1. Significant Responders collectively decreased; 2. Insignificant Responders varied; and 3. Non-Responders were largely stable. Pre-therapy, all 15 cases demonstrated AI-enabled chest x-ray evidence of diastolic dysfunction; post-therapy, 4 improved (especially cases of greatest weight loss or hemoglobin A1c reduction), 5 were stable, and 6 worsened. Per subgroup, AI-enabled chest x-ray indications therapeutic response were: 1. Significant Responders (all demonstrating unequivocally decreased obesity and improved diabetes) collectively showed stable-decreased dysfunction; 2. Insignificant Responders (all demonstrating minimally decreased obesity but stable-worsening diabetes in most) reflected stable-increased dysfunction in 60%; and 3. Non-Responders (all demonstrating increased obesity but stable-improved diabetes) showed increased dysfunction in 80%. Conclusion AI-enabled CXR staging detects background subclinical diastolic dysfunction in diabesity and monitors its response to incretin-based therapy. Diabesity Pulmonary venous hypertension Pulmonary congestion Diastolic dysfunction Incretin-based Therapy GLP-1 receptor agonist GIP receptor agonist Figures Figure 1 Introduction Obesity [Ob] promotes type-2 Diabetes Mellitus [DM] and the combined “diabesity” has become a major cause of Heart Failure [HF]. 1 Especially when blended, OB and DM are myocardial lipotoxic due to increased free fatty acids released from Visceral Adipose Tissue [VAT] being deposited in cardiomyocytes at levels exceeding cellular-storage capacity and insulin resistance-driven demands for fatty-acid oxidation. 1 Consequently, internally leaked deleterious lipid intermediates induce endoplasmic reticulum and mitochondrial injury, inflammatory responses, and cell apoptosis. 1 Associated fibrotic reactions intensify energetics-disrupted myocardial stiffening in causing subclinical Left Ventricular Diastolic Dysfunction [LVDD]. 1 Due to central locations and/or unique composition, relatively greater lipotoxicity from surrounding Epicardial Adipose Tissue [EAT] and Pericardial Adipose Tissue [PAT] accentuate the direct assault on LV function, while also exacerbating LVDD via surrounding physical restraint of diastolic filling. 1 The benefits of incretin-based therapy with Glucagon-Like Peptide-1 [GLP-1] or combined GLP-1/Glucose-dependent Insulinotropic Polypeptide [GIP] Receptor Agonists [RAs] on diabesity-challenged cardiovascular systems are well-recognized. 2 While GLP-1 or GLP-1/GIP RAs improve symptoms related to diabesity-associated HF with preserved Ejection Fraction [HFpEF] 2 , their impacts on the asymptomatic pre-HF phase clinically or its anticipated progression to HFpEF 3 have not been delineated. A practical approach to regular evaluation of LV diastolic function in diabesity patients undergoing incretin-based therapy could facilitate early subclinical LVDD detection and optimize efforts to arrest/reverse progression by medical modification of related HF risk factors. 4 Consequently. we evaluated LVDD indicators provided by Artificial Intelligence [AI]-enabled Chest X-Ray [CXR] staging of Pulmonary Venous Hypertension [PVH] 5 (aka “pulmonary congestion”) in diabesity patients before and after incretin-based therapy. We hypothesized that improved diabesity profiles would be manifested by CXR indications of improved LV filling. Methodology An Institutional Review Board-approved (including waived consent) search of the enterprise electronic medical record identified the select group of diabesity patients meeting the following inclusion criteria: 1. > 12 months of continuous GLP-1 and/or GLP-1/GIP RA therapy 2. Non-portable digital CXR examinations both within 1 month before diabesity-therapy initiation, as well as within 1 month of completion or after > 12 months of ongoing therapy (if > 1 CXR was applicable, that latest was used) 3. No known or suspected ventricular or valvular dysfunction Following exclusion of patients with conditions either potentially confounding evaluations of either LVDD (1 each: atrial fibrillation; volume-overloading from chronic kidney disease stage 4-5) or pulmonary vasculature pattern (1 each: secondary pulmonary hypertension; pulmonary fibrosis) 5 , the remaining 15 cases constituted the final study group. In each case, the following diabesity descriptors before and after incretin-based therapy were recorded: 1. Weight (Kg) and Body Mass Index [BMI] (Kg/M 2 : https://www.cdc.gov/bmi/adult-calculator/index.html) 2. Hemoglobin A1c [HbA1c] (Normal = 4.0-5.6%, pre-DM = 5.7-6.4%, and DM > 6.5% https://www.mayoclinic.org/tests-procedures/a1c-test/about/pac-20384643). In addition, the probabilities of absent versus present PVH at either Stage 1 (vascular distention/redistribution with minimal interstitial edema) or Stage > 2 (vascular congestion with > mild interstitial or alveolar edema) were determined using AI-enabled CXR staging, serving as surrogates for tendencies in mean Left Atrial Pressure [mLAP] 6 , previously validated for tracking LVDD grades by Doppler echocardiography. 5 Results The demographic and background diabesity profiles, as well as subsequent therapies, in the 15 study cases are delineated in Table 1 . By BMI-based categorization of adults ( https://www.cdc.gov/bmi/adult-calculator/bmi-categories.html ) within 1 week of therapy initiation, Cases 1–6 (BMI 41.0-62.6) had Class-3, Cases 7–11 (BMI 35.4–39.8) had Class-2, and Cases 12–14 (BMI 31.4–33.7) had Class-1 Ob, while pre-diabesity Case 15 was overweight (BMI 25.3). Within 2 months of the therapy start, Cases 1–14 demonstrated abnormally elevated HbA1c levels (6.2–12.3%) indicating concurrent pre-DM (2 cases) or DM (12 cases) ( https://www.cdc.gov/diabetes/diabetes-testing/prediabetes-a1c-test.html ); the pre-diabesity case had a high-normal fasting glucose level (98 mg/dL). Diabesity therapies in the 15 cases spanned 12–46 months between points of CXR monitoring. They included use of a GLP-1 RA (Dulaglutide or Semaglutide) alone (12 cases) or combined GLP-1 RA and GLP-1/GIP RA (3 cases). Table 1 Diabesity Cases (Sorted by Decreasing Pre-Therapy BMI) Demographics Diabesity Profile Before Therapy Initiation Diabesity Therapy Between CXR Monitoring Case # Sex Age (YO) Diabesity Components BMI (Kg/M 2 ) Weight (Kg) (< 1 Wk) HbA1c (Nl 4.0-5.6%) (< 2 Mo) NT-proBNP** (Neg < 300 pg/mL) (< 2 Wk) GLP-1 RA GLP-1/GIP RA (Tirzepatide) Both 1 F 46 Ob, DM* 62.6 181 7.8 X Semaglutide 46 Mo (0.25-1.0 mg) 19 Mo (5.0–15.0 mg) 46 Mo Total (Overlapped 19 Mo) 2 F 54 Ob, Pre-DM 50.0 128 6.3 X Semaglutide 29 Mo (0.25-1.0 mg) X X 3 F 81 Ob, DM* 44.9 105 8.1 X Dulaglutide 101 Mo (0.75–1.5 mg) X X 4 F 36 Ob, DM* 42.2 108 7.1 X Semaglutide 22 Mo (0.25-2.0 mg) X X 5 F 50 Ob, DM* 41.2 99 12.3 X Semaglutide 33 Mo (0.25-1.0 mg) X X 6 F 68 Ob, DM* 41.0 109 8.0 X Semaglutide 54 Mo (0.25-2.0 mg) X X 7 F 36 Ob, DM 39.8 111 9.3 X Semaglutide 13 Mo (0.25-2.0 mg) 10 Mo (5.0-12.5 mg) 13 Mo Total (Overlapped 10 Mo) 8 M 81 Ob, DM* 38.5 122 7.1 X Semaglutide 24 Mo (0.25-1.0 mg) X X 9 M 76 Ob, DM* 35.9 105 9.0 X Semaglutide 32 Mo (0.25-2.0 mg) X X 10 F 64 Ob, DM* 35.4 93 8.6 43 Semaglutide 40 Mo (0.25-2.0 mg) X X 11 M 69 Ob, Pre-DM 35.4 111 6.2 X Semaglutide 24 Mo (0.25-2.0 mg) 12 Mo (5.0-7.5 mg) 36 Mo Total (Sequential) 12 M 82 Ob, DM* 33.7 93 7.1 X Semaglutide 25 Mo (0.25-2.0 mg) X X 13 F 68 Ob, DM* 32.4 81 8.3 X Semaglutide 12 Mo (0.25-1.0 mg) X X 14 M 58 Ob, DM* 31.4 95 11.3 X Dulaglutide 27 Mo (1.5–4.5 mg) X X 15 F 75 Pre-Ob/DM* 25.3 68 FGluc 98 mg/dL 85 Semaglutide 15 Mo (0.25-3.0 mg) X X Legend Units: dL = deciLiters General: BMI = Body Mass Index Nl = Normal Kg = Kilograms DM = type 2 Diabetes Mellitus NT-proBNP = N-terminal pro-B-type natriuretic peptide mg = milligrams F = Female Ob = Obesity mL = milliLiters FGluc = Fasting Glucose * = added history of Hypertension Mo = Months GLP-1 = Glucagon-Like Peptide-1 ** = concurrent Doppler echocardiography (< 1 Mo of therapy M 2 = Meters 2 GIP = Glucose-dependent Insulinotropic Polypeptide initiation) had not been performed pg = picograms Wk = Weeks HbA1c = Hemoglobin A1c M = Male YO = Years Old Neg = Negative Table 2: Diabesity Cases After Incretin-Based Therapy (Sorted by Decreasing Post-Therapy Weight Loss) Case # Post-Therapy Diabesity Profiles AI-Enabled CXR PVH Staging Weight Change Hb A1c Level Change Kg % Kg & BMI Decreased : • by 0.5–0.9% point [ < ] • by 1.0-1.9% point [ << ] • by 2.0-2.9% point [ <<< ] • by ≥ 3% point [ <<<> ] Stable : [ o ] Initial PVH Stage (Highest Probability) Post-Therapy PVH Stage (Highest Probability) Decreased Highest Probability : • at same Stage by ≥ 0.10 [ < ] or • transitioning to lower Stage [ < ] or • transitioning to higher Stage [ >> ] Stable : [ o ] Significant Responders 3 − 32 − 30 < ≥ 2 (0.90) 1 (0.68) << 4 − 23 − 21 <<* 1 (0.55) 1 (0.62) o 1 − 22 − 12 << ≥ 2 (0.99) ≥ 2 (0.80) < 11 − 11 − 10 <* 1 (0.55) 1 (0.58) o 6 − 10 − 9 <> 1 (0.71) ≥ 2 (0.87) >> 7 − 2 − 2 o 1(0.58) 1 (0.66) o 14 − 1 − 1 <<<< ≥ 2 (0.90) 1 (0.87) <> ≥ 2 (0.87) ≥ 2 (0.74) < 13 0 0 > Non-Responders 5 + 2 + 2 o 1 (0.62) 1 (0.80) > 8 + 3 + 2 > 9 + 6 + 6 << 10 + 6 + 6 o 1 (0.56) ≥ 2 (0.92) >> 15 + 8 + 12 N/A 1 (0.57) 1 (0.53) o Legend Units: Kg = Kilograms General: AI = Artificial Intelligence BMI = Body Mass Index CXR = Chest X-Ray GLP-1 = Glucagon-Like Peptide-1 GIP = Glucose-dependent Insulinotropic Polypeptide HbA1c = Hemoglobin A1c N/A = Not Applicable PVH = Pulmonary Venous Hypertension The post-therapy diabesity profiles, as well as pre- versus post- therapy AI-enabled CXR PVH staging results, are outlined in Table 2. According to weight-loss, the cases clustered into three distinct equal-sized subgroups as follows: 1. Significant Responders (9–30% Kg & BMI decreases, consistent with target levels of 10–20% for incretin-based therapy) 7 ; 2. Insignificant Responders (0–2% Kg & BMI decreases); and 3. Non-Responders (2–12% Kg & BMI increases). All Significant Responders also demonstrated HbA1c reductions, including mild (0.5–0.9% point) in 2 cases or moderate (1.0-1.9% point) in 3 cases 8 , twice achieving normal levels. Conversely, Insignificant Responders showed varied HbA1c changes, including decreases (1 mild but 1 pronounced ≥ 3%-point) in 2 cases versus moderate increases in 2 cases. Last, HbA1c levels were largely stable in Non-Responders, except for 2 reductions (1 mild and 1 large 2.0-2.9%-point). All 15 cases exhibited background pre-therapy evidence (current or intermittent) LVDD-related mLAP elevations by AI-enabled CXR determinations of PVH 5 , either at Stage 1 in 11 cases or Stage ≥ 2 in 4 cases [Table 2]. With diabesity therapy, 4 cases demonstrated CXR evidence of decreased mLAP elevation based on: 1. Decreased likelihood (i.e., highest probability reduced by ≥ 0.10) at the same PVH Stage alone (2 cases); or 2. Prominent decreased PVH likelihood causing downward transitioning from Stage ≥ 2 to Stage 1 (2 cases, including Significant Responder Case 3 with greatest weight loss of 30% [Figure 1 a]; and Insignificant Responder Case 14 with greatest HbA1c decrease). In contrast, 6 cases supported mLAP elevation based on: 1. Increased likelihood of the same PVH Stage alone (2 cases); or 2. Prominent increased PVH likelihood causing upward transitioning from Stage 1 to Stage ≥ 2 (2 Insignificant responders and 2 Non-Responders [Figure 1 b]). The remaining 5 cases, including 3 Significant Responders, demonstrated stable mLAP elevation with therapy. Thus, per subgroup, the following patterns in LVDD-related mLAP elevation response to diabesity therapy were observed [Table 2]: 1. Significant Responders (all unequivocally demonstrating both weight loss and improved DM, while retaining post-therapy Ob-level BMI 31.4–55.1) collectively showed encouraging stable-decreased mLAP elevations (especially Case 3); 2. Insignificant Responders (all securing post-therapy Ob-level BMI 31.1–49.0 but stable-worsening DM in most) reflected stable-increased mLAP elevations in the majority (60%); and 3. Non-Responders (all demonstrating advancing Ob levels contrasting with stable-improved DM) showed evidence of progressing mLAP elevations in the large majority (80%, the exception being the initially pre-diabesity case). Discussion This report provides supporting evidence of collective background subclinical LVDD along a diabesity spectrum, possibly representing asymptomatic preclinical-HF 1 ; this insight was provided using AI-enabled CXR recognition of PVH stages reflecting mLAP elevations. 5 , 6 These CXR capabilities also indicated significantly decreased mLAP elevations following pronounced improvement in Ob and/or DM conditions after incretin-based therapy with GLP-1 or GLP-1/GIP RAs. 2 Lesser levels of combined weight-loss and DM-control appeared to promote LVDD stabilization, while unchanged-to-gained weight or worsening DM-status despite therapy appeared to enable LVDD progression, possibly towards HFpEF. Limitations The retrospective data collection was a recognized but unavoidable limitation of this proof-of-concept study design. Likewise, the small size of the final study group was an uncontrollable consequence of the lack, until now, of evidence-based endorsement of CXR monitoring during diabesity therapy which would be a practical (e.g., affordable, widely available) care enhancement. Last, this study did not incorporate imaging of VAT, EAT, or PAT deposit changes from therapy. Conclusion AI-enabled CXR PVH staging, reflecting LVDD-related mLAP elevation, detects background subclinical LVDD characterizing diabesity and monitors its evolution (improvement, stabilization, or progression) during incretin-based therapy. Abbreviations AI Artificial Intelligence BMI Body Mass Index CXR Chest X-Ray DM type-2 Diabetes Mellitus EAT Epicardial Adipose Tissue GIP Glucose-dependent Insulinotropic Polypeptide GLP-1 Glucagon-Like Peptide-1 HbA1c Hemoglobin A1c HF Heart Failure HFpEF HF with preserved Ejection Fraction LVDD Left Ventricular Diastolic Dysfunction mLAP mean Left Atrial Pressure Ob Obesity PAT Pericardial Adipose Tissue PVH Pulmonary Venous Hypertension RAs Receptor Agonists VAT Visceral Adipose Tissue Declarations Ethics Approval and Consent to Participate: The needed compiling and analysis of the focused data mined from the Mayo Clinic enterprise electronic medical record, with waived requirement for patient consenting, was performed with prior approval from the Mayo Clinic Institutional Review Board. No Generative-AI technology was used in the performance of this research or the preparation of this Short Report. Consent for Publication: The use of the de-identified or anonymized data in this Short Report was approved by the Mayo Clinic Institutional Review Board. Availability of Data and Materials: No established databases were utilized in support of this study. Complete image datasets are not made publicly available for non-federally funded research due to Mayo Clinic in­stitutional restrictions. However, the high-resolution CXR images used in this study may be available to interested researchers upon request from the Center for Augmented Intelligence in Imaging of the Mayo Clinic Florida (contact: [email protected] ). Competing Interests: An application for a provisional patent on the AI-enable CXR methodology applied in this study (https://pubmed.ncbi.nlm.nih.gov/41174036/) has been submitted by the Mayo Clinic on behalf of White, Demirer, and Erdal. Otherwise, the four co-authors individually have no potential conflicts of interest to disclose. Funding: This work was entirely supported internally by the Department of Radiology, Mayo Clinic Florida; no external funding was used. Authors’ Contributions: Individual author contributions are as follows: RDW: Supervised all aspects from Concept development to Short Report submission MD: Concept confirmation, CXR & AI-model processing, Short Report review/revision BSE: Concept confirmation, case data mining, AI-model processing, Short Report review/revision GL: Concept confirmation, case data analysis, Short Report review/revision All authors read and approved the final version of the Short Report for submission. Acknowledgements: Not Applicable Authors’ Information: RDW: Cardiovascular imager since mid-1980’s with MS in imaging-AI / Medical Director-Center for Augmented Intelligence in Imaging at Mayo Clinic Florida MD: Computer engineer with AI subspecialization BSE: Electrical and computer engineer with data-mining and AI subspecialization / Technical Director-Center for Augmented Intelligence in Imaging at Mayo Clinic Florida GL: Chair-Cardiovascular Medicine at Mayo Clinic Florida with heart failure subspecialization References Ng ACT, Delgado V, Borlaug BA, Bax JJ, Diabesity. The combined burden of obesity and diabetes on heart disease and the role of imaging. Nat Rev Cardiol. 2021;18(4):291–304. 10.1038/s41569-020-00465-5 . Epub 2020 Nov 13. PMID: 33188304. Nauck MA, Tuttle KR, Tschöp MH, Blüher M. Glucagon-like receptor agonists and next-generation incretin-based medications: Metabolic, cardiovascular, and renal benefits. Lancet. 2026 Jan 14:S0140-6736(25)02105-1. 10.1016/S0140-6736(25)02105-1 . Epub ahead of print. PMID: 41547366. Kosmala W, Marwick TH. Asymptomatic left ventricular diastolic dysfunction: Predicting progression to symptomatic heart failure. JACC Cardiovasc Imaging. 2020;13(1 Pt 2):215–27. 10.1016/j.jcmg.2018.10.039 . Epub 2019 Apr 17. PMID: 31005530. Kim DG, Cho S, Park S, Kim GR, Ko KY, Kim SE, Hwang JW, Doh JH, Kwon SU, Kwak JJ, Namgung J, Cho SW. Predisposing risk factors affecting reversibility of left ventricular diastolic filling pattern in patients with preserved ejection fraction. Yonsei Med J. 2025;66(1):1–8. 10.3349/ymj.2023.0410 . PMID: 39742879; PMCID: PMC11704242. White RD, Demirer M, Sebro RA, Cortopassi IO, Stowell JT, McCann MR, Barry T, Appleton CP, Helgeson SA, Erdal BS. Artificial intelligence improves detection and classification of pulmonary venous hypertension related to left ventricular diastolic dysfunction by chest radiography. Sci Rep. 2025;15(1):38181. 10.1038/s41598-025-22026-x . PMID: 41174036; PMCID: PMC12579234. Peverill RE. Left ventricular filling pressure(s) - Ambiguous and misleading terminology, best abandoned. Int J Cardiol. 2015;191:110-3. 10.1016/j.ijcard.2015.04.254 . Epub 2015 May 1. PMID: 25965616. Kittleson MM, Benjamin EJ, Blumer V, Harrington J, Januzzi JL, McMurray JJV, Vest AR. 2025 ACC Scientific Statement on the Management of Obesity in Adults With Heart Failure: A report of the American College of Cardiology. J Am Coll Cardiol. 2025;86(20):1953–1975. doi: 10.1016/j.jacc.2025.05.008. Epub 2025 Jun 13. PMID: 40512113. Sherifali D, Nerenberg K, Pullenayegum E, Cheng JE, Gerstein HC. The effect of oral antidiabetic agents on A1C levels: A systematic review and meta-analysis. Diabetes Care. 2010;33(8):1859–64. 10.2337/dc09-1727 . Epub 2010 May 18. PMID: 20484130; PMCID: PMC2909079. Additional Declarations Competing interest reported. Other than an application filed by the Mayo Clinic (including White, Demirer, and Erdal) for a provisional patent to protect the used AI methodology while we gain real-world experience such as this, the authors have no potential competing interests to declare. 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Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9272120","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":616865151,"identity":"109aab4b-5eac-4106-a1a1-de3df7dc720a","order_by":0,"name":"Richard D. White","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACCcYGEMXDIAEiDWygwmx4tTQ2ILQUpBGjhQFiDUTLh8OEtRjcbm5/8IHhngz/7OZnH94YnI/mn5F8gOFD2WHcWu4cbGycwVDMI3HnmPHMOQa3c2fcSEtgnHEOtxazG4mNzTwMCTwGEgnGzDxALQ03cgyYedsIaPkD1pL+GajlXO58kJa/hLQwgLXkgGw5kLsBpIURjxZ7oJaZPQYJPBI3cooZ5xgk52488yzhYM+5dJxaJGekP/jwoyLBnn9G+maGN3/scucdTz744EeZNU4tEGCAzBFIYDhAQD064CdVwygYBaNgFAx3AACGxVpaklNdQwAAAABJRU5ErkJggg==","orcid":"","institution":"Mayo Clinic","correspondingAuthor":true,"prefix":"","firstName":"Richard","middleName":"D.","lastName":"White","suffix":""},{"id":616865160,"identity":"36525886-a055-4d80-9841-b14fefea4f35","order_by":1,"name":"Mutlu Demirer","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Mutlu","middleName":"","lastName":"Demirer","suffix":""},{"id":616865163,"identity":"076a6bb4-ad9f-4dc6-9961-07611608f1cc","order_by":2,"name":"Barbaros Selnur Erdal","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Barbaros","middleName":"Selnur","lastName":"Erdal","suffix":""},{"id":616865164,"identity":"21d8bc7e-206a-464d-8399-019f725a1fa3","order_by":3,"name":"Grace Lin","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Grace","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2026-03-30 22:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9272120/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9272120/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106402779,"identity":"f8073f1b-fbe7-4dc7-ac08-adb0eefea05a","added_by":"auto","created_at":"2026-04-08 09:12:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":780647,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9272120/v1/d9e6320a882ad875f1787963.png"},{"id":106405751,"identity":"f982ea0d-7427-4954-99d7-e8f8610bce31","added_by":"auto","created_at":"2026-04-08 09:28:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1964814,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9272120/v1/a605d146-82b5-4b21-9a94-06f19649d5a2.pdf"},{"id":106190078,"identity":"4265d87b-ec54-4305-9b3e-ef5b1a233f41","added_by":"auto","created_at":"2026-04-05 17:13:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13972,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9272120/v1/7d975d0c1a1b079f9ef8bcb7.docx"}],"financialInterests":"Competing interest reported. Other than an application filed by the Mayo Clinic (including White, Demirer, and Erdal) for a provisional patent to protect the used AI methodology while we gain real-world experience such as this, the authors have no potential competing interests to declare.","formattedTitle":"AI-Enabled Chest X-Ray Detects Subclinical Diastolic Dysfunction in Diabesity and its Therapeutic Responses to GLP-1 or GLP-1/GIP Receptor Agonists","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity [Ob] promotes type-2 Diabetes Mellitus [DM] and the combined \u0026ldquo;diabesity\u0026rdquo; has become a major cause of Heart Failure [HF].\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Especially when blended, OB and DM are myocardial lipotoxic due to increased free fatty acids released from Visceral Adipose Tissue [VAT] being deposited in cardiomyocytes at levels exceeding cellular-storage capacity and insulin resistance-driven demands for fatty-acid oxidation.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Consequently, internally leaked deleterious lipid intermediates induce endoplasmic reticulum and mitochondrial injury, inflammatory responses, and cell apoptosis.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Associated fibrotic reactions intensify energetics-disrupted myocardial stiffening in causing subclinical Left Ventricular Diastolic Dysfunction [LVDD].\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Due to central locations and/or unique composition, relatively greater lipotoxicity from surrounding Epicardial Adipose Tissue [EAT] and Pericardial Adipose Tissue [PAT] accentuate the direct assault on LV function, while also exacerbating LVDD via surrounding physical restraint of diastolic filling.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe benefits of incretin-based therapy with Glucagon-Like Peptide-1 [GLP-1] or combined GLP-1/Glucose-dependent Insulinotropic Polypeptide [GIP] Receptor Agonists [RAs] on diabesity-challenged cardiovascular systems are well-recognized.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e While GLP-1 or GLP-1/GIP RAs improve symptoms related to diabesity-associated HF with preserved Ejection Fraction [HFpEF]\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, their impacts on the asymptomatic pre-HF phase clinically or its anticipated progression to HFpEF\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e have not been delineated. A practical approach to regular evaluation of LV diastolic function in diabesity patients undergoing incretin-based therapy could facilitate early subclinical LVDD detection and optimize efforts to arrest/reverse progression by medical modification of related HF risk factors.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eConsequently. we evaluated LVDD indicators provided by Artificial Intelligence [AI]-enabled Chest X-Ray [CXR] staging of Pulmonary Venous Hypertension [PVH]\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e (aka \u0026ldquo;pulmonary congestion\u0026rdquo;) in diabesity patients before and after incretin-based therapy. We hypothesized that improved diabesity profiles would be manifested by CXR indications of improved LV filling.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eAn Institutional Review Board-approved (including waived consent) search of the enterprise electronic medical record identified the select group of diabesity patients meeting the following inclusion criteria: \u003c/p\u003e\n\u003cp\u003e1. \u003cu\u003e\u0026gt;\u003c/u\u003e 12 months of continuous GLP-1 and/or GLP-1/GIP RA therapy \u003c/p\u003e\n\u003cp\u003e2. Non-portable digital CXR examinations both within 1 month before diabesity-therapy initiation, as well as within 1 month of completion or after \u003cu\u003e\u0026gt;\u003c/u\u003e 12 months of ongoing therapy (if \u0026gt; 1 CXR was applicable, that latest was used) \u003c/p\u003e\n\u003cp\u003e3. No known or suspected ventricular or valvular dysfunction \u003c/p\u003e\n\u003cp\u003eFollowing exclusion of patients with conditions either potentially confounding evaluations of either LVDD (1 each: atrial fibrillation; volume-overloading from chronic kidney disease stage 4-5) or pulmonary vasculature pattern (1 each: secondary pulmonary hypertension; pulmonary fibrosis)\u003csup\u003e5\u003c/sup\u003e, the remaining 15 cases constituted the final study group.\u003c/p\u003e\n\u003cp\u003eIn each case, the following diabesity descriptors before and after incretin-based therapy were recorded: \u003c/p\u003e\n\u003cp\u003e1. Weight (Kg) and Body Mass Index [BMI] (Kg/M\u003csup\u003e2\u003c/sup\u003e: https://www.cdc.gov/bmi/adult-calculator/index.html)\u003c/p\u003e\n\u003cp\u003e2. Hemoglobin A1c [HbA1c] (Normal = 4.0-5.6%, pre-DM = 5.7-6.4%, and DM \u003cu\u003e\u0026gt;\u003c/u\u003e 6.5% https://www.mayoclinic.org/tests-procedures/a1c-test/about/pac-20384643). \u003c/p\u003e\n\u003cp\u003eIn addition, the probabilities of absent versus present PVH at either Stage 1 (vascular distention/redistribution with minimal interstitial edema) or Stage \u003cu\u003e\u0026gt;\u003c/u\u003e 2 (vascular congestion with \u003cu\u003e\u0026gt;\u003c/u\u003e mild interstitial or alveolar edema) were determined using AI-enabled CXR staging, serving as surrogates for tendencies in mean Left Atrial Pressure [mLAP]\u003csup\u003e6\u003c/sup\u003e, previously validated for tracking LVDD grades by Doppler echocardiography.\u003csup\u003e5\u003c/sup\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe demographic and background diabesity profiles, as well as subsequent therapies, in the 15 study cases are delineated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. By BMI-based categorization of adults (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/bmi/adult-calculator/bmi-categories.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/bmi/adult-calculator/bmi-categories.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) within 1 week of therapy initiation, Cases 1\u0026ndash;6 (BMI 41.0-62.6) had Class-3, Cases 7\u0026ndash;11 (BMI 35.4\u0026ndash;39.8) had Class-2, and Cases 12\u0026ndash;14 (BMI 31.4\u0026ndash;33.7) had Class-1 Ob, while pre-diabesity Case 15 was overweight (BMI 25.3). Within 2 months of the therapy start, Cases 1\u0026ndash;14 demonstrated abnormally elevated HbA1c levels (6.2\u0026ndash;12.3%) indicating concurrent pre-DM (2 cases) or DM (12 cases) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/diabetes/diabetes-testing/prediabetes-a1c-test.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/diabetes/diabetes-testing/prediabetes-a1c-test.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); the pre-diabesity case had a high-normal fasting glucose level (98 mg/dL). Diabesity therapies in the 15 cases spanned 12\u0026ndash;46 months between points of CXR monitoring. They included use of a GLP-1 RA (Dulaglutide or Semaglutide) alone (12 cases) or combined GLP-1 RA and GLP-1/GIP RA (3 cases).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiabesity Cases (Sorted by Decreasing Pre-Therapy BMI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eDiabesity Profile Before Therapy Initiation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eDiabesity Therapy Between CXR Monitoring\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCase #\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(YO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDiabesity Components\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Kg/M\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eWeight\u003c/b\u003e (Kg)\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;1 Wk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eHbA1c\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Nl 4.0-5.6%)\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;2 Mo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eNT-proBNP**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Neg\u0026thinsp;\u0026lt;\u0026thinsp;300 pg/mL)\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;2 Wk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eGLP-1 RA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eGLP-1/GIP RA\u003c/b\u003e (Tirzepatide)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eBoth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e46 Mo (0.25-1.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19 Mo\u003c/p\u003e \u003cp\u003e(5.0\u0026ndash;15.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e46 Mo Total\u003c/p\u003e \u003cp\u003e(Overlapped 19 Mo)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, Pre-DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e29 Mo (0.25-1.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDulaglutide\u003c/p\u003e \u003cp\u003e101 Mo (0.75\u0026ndash;1.5 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e22 Mo (0.25-2.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e33 Mo (0.25-1.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e54 Mo (0.25-2.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e13 Mo (0.25-2.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10 Mo\u003c/p\u003e \u003cp\u003e(5.0-12.5 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13 Mo Total\u003c/p\u003e \u003cp\u003e(Overlapped 10 Mo)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e24 Mo (0.25-1.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e32 Mo (0.25-2.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e40 Mo (0.25-2.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, Pre-DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e24 Mo (0.25-2.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12 Mo\u003c/p\u003e \u003cp\u003e(5.0-7.5 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e36 Mo Total\u003c/p\u003e \u003cp\u003e(Sequential)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e25 Mo (0.25-2.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e12 Mo (0.25-1.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOb, DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDulaglutide\u003c/p\u003e \u003cp\u003e27 Mo (1.5\u0026ndash;4.5 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePre-Ob/DM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFGluc 98 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003cp\u003e15 Mo (0.25-3.0 mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLegend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eUnits: dL\u0026thinsp;=\u0026thinsp;deciLiters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003eGeneral: BMI\u0026thinsp;=\u0026thinsp;Body Mass Index Nl\u0026thinsp;=\u0026thinsp;Normal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eKg\u0026thinsp;=\u0026thinsp;Kilograms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003eDM\u0026thinsp;=\u0026thinsp;type 2 Diabetes Mellitus NT-proBNP\u0026thinsp;=\u0026thinsp;N-terminal pro-B-type natriuretic peptide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003emg\u0026thinsp;=\u0026thinsp;milligrams\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;Female Ob\u0026thinsp;=\u0026thinsp;Obesity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003emL\u0026thinsp;=\u0026thinsp;milliLiters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003eFGluc\u0026thinsp;=\u0026thinsp;Fasting Glucose * = added history of Hypertension\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMo\u0026thinsp;=\u0026thinsp;Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003eGLP-1\u0026thinsp;=\u0026thinsp;Glucagon-Like Peptide-1 ** = concurrent Doppler echocardiography (\u0026lt;\u0026thinsp;1 Mo of therapy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eM\u003csup\u003e2\u003c/sup\u003e = Meters\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003eGIP\u0026thinsp;=\u0026thinsp;Glucose-dependent Insulinotropic Polypeptide initiation) had not been performed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003epg = picograms\u003c/p\u003e \u003cp\u003eWk\u0026thinsp;=\u0026thinsp;Weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003eHbA1c\u0026thinsp;=\u0026thinsp;Hemoglobin A1c\u003c/p\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;Male\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eYO\u0026thinsp;=\u0026thinsp;Years Old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003eNeg\u0026thinsp;=\u0026thinsp;Negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;2: Diabesity Cases After Incretin-Based Therapy (Sorted by Decreasing Post-Therapy Weight Loss)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eCase\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e#\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePost-Therapy Diabesity Profiles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c9\" namest=\"c7\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAI-Enabled CXR PVH Staging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWeight Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eHb A1c Level Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eKg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eKg\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026amp;\u003c/p\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eDecreased\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u0026bull; by 0.5\u0026ndash;0.9% point [\u003cb\u003e\u0026lt;\u003c/b\u003e]\u003c/p\u003e \u003cp\u003e\u0026bull; by 1.0-1.9% point [\u003cb\u003e\u0026lt;\u0026lt;\u003c/b\u003e]\u003c/p\u003e \u003cp\u003e\u0026bull; by 2.0-2.9% point [\u003cb\u003e\u0026lt;\u0026lt;\u0026lt;\u003c/b\u003e]\u003c/p\u003e \u003cp\u003e\u0026bull; by \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;3% point [\u003cb\u003e\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/b\u003e]\u003c/p\u003e \u003cp\u003eand/or\u003c/p\u003e \u003cp\u003eto Normal (4.0-5.6%) [\u003cb\u003e*\u003c/b\u003e]\u003c/p\u003e \u003cp\u003e\u003cb\u003eIncreased\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u0026bull; by 1.0-1.9% point [\u003cb\u003e\u0026gt;\u0026gt;\u003c/b\u003e]\u003c/p\u003e \u003cp\u003e\u003cb\u003eStable\u003c/b\u003e: [\u003cb\u003eo\u003c/b\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eInitial\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePVH Stage\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Highest Probability)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ePost-Therapy\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePVH Stage\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(Highest Probability)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eDecreased Highest Probability\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u0026bull; at same Stage by \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.10 [\u003cb\u003e\u0026lt;\u003c/b\u003e]\u003c/p\u003e \u003cp\u003eor\u003c/p\u003e \u003cp\u003e\u0026bull; transitioning to lower Stage [\u003cb\u003e\u0026lt;\u0026lt;\u003c/b\u003e]\u003c/p\u003e \u003cp\u003e\u003cb\u003eIncreased Highest Probability\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u0026bull; at same Stage by \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.10 [\u003cb\u003e\u0026gt;\u003c/b\u003e]\u003c/p\u003e\u003cp\u003eor\u003c/p\u003e\u003cp\u003e\u0026bull; transitioning to higher Stage [\u003cb\u003e\u0026gt;\u0026gt;\u003c/b\u003e]\u003c/p\u003e \u003cp\u003e\u003cb\u003eStable\u003c/b\u003e: [\u003cb\u003eo\u003c/b\u003e]\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eSignificant Responders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026lt;*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eInsignificant Responders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1(0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eNon-Responders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026lt;\u0026lt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLegend\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUnits: Kg\u0026thinsp;=\u0026thinsp;Kilograms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral: AI\u0026thinsp;=\u0026thinsp;Artificial Intelligence\u003c/p\u003e \u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;Body Mass Index\u003c/p\u003e \u003cp\u003eCXR\u0026thinsp;=\u0026thinsp;Chest X-Ray\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLP-1\u0026thinsp;=\u0026thinsp;Glucagon-Like Peptide-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIP\u0026thinsp;=\u0026thinsp;Glucose-dependent Insulinotropic Polypeptide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u0026thinsp;=\u0026thinsp;Hemoglobin A1c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN/A\u0026thinsp;=\u0026thinsp;Not Applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVH\u0026thinsp;=\u0026thinsp;Pulmonary Venous Hypertension\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe post-therapy diabesity profiles, as well as pre- versus post- therapy AI-enabled CXR PVH staging results, are outlined in Table\u0026nbsp;2. According to weight-loss, the cases clustered into three distinct equal-sized subgroups as follows: 1. Significant Responders (9\u0026ndash;30% Kg \u0026amp; BMI decreases, consistent with target levels of 10\u0026ndash;20% for incretin-based therapy)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e; 2. Insignificant Responders (0\u0026ndash;2% Kg \u0026amp; BMI decreases); and 3. Non-Responders (2\u0026ndash;12% Kg \u0026amp; BMI increases).\u003c/p\u003e \u003cp\u003eAll Significant Responders also demonstrated HbA1c reductions, including mild (0.5\u0026ndash;0.9% point) in 2 cases or moderate (1.0-1.9% point) in 3 cases\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, twice achieving normal levels. Conversely, Insignificant Responders showed varied HbA1c changes, including decreases (1 mild but 1 pronounced\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;3%-point) in 2 cases versus moderate increases in 2 cases. Last, HbA1c levels were largely stable in Non-Responders, except for 2 reductions (1 mild and 1 large 2.0-2.9%-point).\u003c/p\u003e \u003cp\u003eAll 15 cases exhibited background pre-therapy evidence (current or intermittent) LVDD-related mLAP elevations by AI-enabled CXR determinations of PVH\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, either at Stage 1 in 11 cases or Stage\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 in 4 cases [Table\u0026nbsp;2]. With diabesity therapy, 4 cases demonstrated CXR evidence of decreased mLAP elevation based on: 1. Decreased likelihood (i.e., highest probability reduced by \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.10) at the same PVH Stage alone (2 cases); or 2. Prominent decreased PVH likelihood causing downward transitioning from Stage\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 to Stage 1 (2 cases, including Significant Responder Case 3 with greatest weight loss of 30% [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea]; and Insignificant Responder Case 14 with greatest HbA1c decrease). In contrast, 6 cases supported mLAP elevation based on: 1. Increased likelihood of the same PVH Stage alone (2 cases); or 2. Prominent increased PVH likelihood causing upward transitioning from Stage 1 to Stage\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;2 (2 Insignificant responders and 2 Non-Responders [Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb]). The remaining 5 cases, including 3 Significant Responders, demonstrated stable mLAP elevation with therapy.\u003c/p\u003e \u003cp\u003eThus, per subgroup, the following patterns in LVDD-related mLAP elevation response to diabesity therapy were observed [Table\u0026nbsp;2]: 1. Significant Responders (all unequivocally demonstrating both weight loss and improved DM, while retaining post-therapy Ob-level BMI 31.4\u0026ndash;55.1) collectively showed encouraging stable-decreased mLAP elevations (especially Case 3); 2. Insignificant Responders (all securing post-therapy Ob-level BMI 31.1\u0026ndash;49.0 but stable-worsening DM in most) reflected stable-increased mLAP elevations in the majority (60%); and 3. Non-Responders (all demonstrating advancing Ob levels contrasting with stable-improved DM) showed evidence of progressing mLAP elevations in the large majority (80%, the exception being the initially pre-diabesity case).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis report provides supporting evidence of collective background subclinical LVDD along a diabesity spectrum, possibly representing asymptomatic preclinical-HF\u003csup\u003e1\u003c/sup\u003e; this insight was provided using AI-enabled CXR recognition of PVH stages reflecting mLAP elevations.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e These CXR capabilities also indicated significantly decreased mLAP elevations following pronounced improvement in Ob and/or DM conditions after incretin-based therapy with GLP-1 or GLP-1/GIP RAs.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Lesser levels of combined weight-loss and DM-control appeared to promote LVDD stabilization, while unchanged-to-gained weight or worsening DM-status despite therapy appeared to enable LVDD progression, possibly towards HFpEF.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitations\u003c/strong\u003e \u003cp\u003eThe retrospective data collection was a recognized but unavoidable limitation of this proof-of-concept study design. Likewise, the small size of the final study group was an uncontrollable consequence of the lack, until now, of evidence-based endorsement of CXR monitoring during diabesity therapy which would be a practical (e.g., affordable, widely available) care enhancement. Last, this study did not incorporate imaging of VAT, EAT, or PAT deposit changes from therapy.\u003c/p\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAI-enabled CXR PVH staging, reflecting LVDD-related mLAP elevation, detects background subclinical LVDD characterizing diabesity and monitors its evolution (improvement, stabilization, or progression) during incretin-based therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtificial Intelligence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCXR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChest X-Ray\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etype-2 Diabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpicardial Adipose Tissue\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlucose-dependent Insulinotropic Polypeptide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLP-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlucagon-Like Peptide-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin A1c\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart Failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHFpEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHF with preserved Ejection Fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft Ventricular Diastolic Dysfunction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emLAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emean Left Atrial Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePericardial Adipose Tissue\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePVH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePulmonary Venous Hypertension\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceptor Agonists\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVisceral Adipose Tissue\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u0026nbsp;\u003c/strong\u003eThe needed compiling and analysis of the focused data mined from the Mayo Clinic enterprise electronic medical record, with waived requirement for patient consenting, was performed with prior approval from the Mayo Clinic Institutional Review Board. \u0026nbsp;No Generative-AI technology was used in the performance of this research or the preparation of this Short Report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eThe use of the de-identified or anonymized data in this Short Report was approved by the Mayo Clinic Institutional Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u0026nbsp;\u003c/strong\u003eNo established databases were utilized in support of this study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eComplete image datasets are not made publicly available for non-federally funded research due to Mayo Clinic in\u0026shy;stitutional restrictions. However, the high-resolution CXR images used in this study may be available to interested researchers upon request from the Center for Augmented Intelligence in Imaging of the Mayo Clinic Florida (contact: [email protected]).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eAn application for a provisional patent on the AI-enable CXR methodology applied in this study (https://pubmed.ncbi.nlm.nih.gov/41174036/) has been submitted by the Mayo Clinic on behalf of White, Demirer, and Erdal. \u0026nbsp;Otherwise, the four co-authors individually have no potential conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was entirely supported internally by the Department of Radiology, Mayo Clinic Florida; no external funding was used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003eIndividual author contributions are as follows:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eRDW: Supervised all aspects from Concept development to Short Report submission\u003c/li\u003e\n \u003cli\u003eMD: Concept confirmation, CXR \u0026amp; AI-model processing, Short Report review/revision\u003c/li\u003e\n \u003cli\u003eBSE: Concept confirmation, case data mining, AI-model processing, Short Report review/revision\u003c/li\u003e\n \u003cli\u003eGL: Concept confirmation, case data analysis, Short Report review/revision\u003c/li\u003e\n \u003cli\u003eAll authors read and approved the final version of the Short Report for submission.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eRDW: Cardiovascular imager since mid-1980\u0026rsquo;s with MS in imaging-AI / Medical Director-Center for Augmented Intelligence in Imaging at Mayo Clinic Florida\u003c/li\u003e\n \u003cli\u003eMD: Computer engineer with AI subspecialization\u003c/li\u003e\n \u003cli\u003eBSE: Electrical and computer engineer with data-mining and AI subspecialization / Technical Director-Center for Augmented Intelligence in Imaging at Mayo Clinic Florida\u003c/li\u003e\n \u003cli\u003eGL: Chair-Cardiovascular Medicine at Mayo Clinic Florida with heart failure subspecialization\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg ACT, Delgado V, Borlaug BA, Bax JJ, Diabesity. The combined burden of obesity and diabetes on heart disease and the role of imaging. Nat Rev Cardiol. 2021;18(4):291\u0026ndash;304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41569-020-00465-5\u003c/span\u003e\u003cspan address=\"10.1038/s41569-020-00465-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2020 Nov 13. PMID: 33188304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNauck MA, Tuttle KR, Tsch\u0026ouml;p MH, Bl\u0026uuml;her M. Glucagon-like receptor agonists and next-generation incretin-based medications: Metabolic, cardiovascular, and renal benefits. Lancet. 2026 Jan 14:S0140-6736(25)02105-1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(25)02105-1\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(25)02105-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub ahead of print. PMID: 41547366.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosmala W, Marwick TH. Asymptomatic left ventricular diastolic dysfunction: Predicting progression to symptomatic heart failure. JACC Cardiovasc Imaging. 2020;13(1 Pt 2):215\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcmg.2018.10.039\u003c/span\u003e\u003cspan address=\"10.1016/j.jcmg.2018.10.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2019 Apr 17. PMID: 31005530.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim DG, Cho S, Park S, Kim GR, Ko KY, Kim SE, Hwang JW, Doh JH, Kwon SU, Kwak JJ, Namgung J, Cho SW. Predisposing risk factors affecting reversibility of left ventricular diastolic filling pattern in patients with preserved ejection fraction. Yonsei Med J. 2025;66(1):1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3349/ymj.2023.0410\u003c/span\u003e\u003cspan address=\"10.3349/ymj.2023.0410\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 39742879; PMCID: PMC11704242.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite RD, Demirer M, Sebro RA, Cortopassi IO, Stowell JT, McCann MR, Barry T, Appleton CP, Helgeson SA, Erdal BS. Artificial intelligence improves detection and classification of pulmonary venous hypertension related to left ventricular diastolic dysfunction by chest radiography. Sci Rep. 2025;15(1):38181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-025-22026-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-22026-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 41174036; PMCID: PMC12579234.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeverill RE. Left ventricular filling pressure(s) - Ambiguous and misleading terminology, best abandoned. Int J Cardiol. 2015;191:110-3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijcard.2015.04.254\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2015.04.254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2015 May 1. PMID: 25965616.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKittleson MM, Benjamin EJ, Blumer V, Harrington J, Januzzi JL, McMurray JJV, Vest AR. 2025 ACC Scientific Statement on the Management of Obesity in Adults With Heart Failure: A report of the American College of Cardiology. J Am Coll Cardiol. 2025;86(20):1953\u0026ndash;1975. doi: 10.1016/j.jacc.2025.05.008. Epub 2025 Jun 13. PMID: 40512113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSherifali D, Nerenberg K, Pullenayegum E, Cheng JE, Gerstein HC. The effect of oral antidiabetic agents on A1C levels: A systematic review and meta-analysis. Diabetes Care. 2010;33(8):1859\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc09-1727\u003c/span\u003e\u003cspan address=\"10.2337/dc09-1727\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2010 May 18. PMID: 20484130; PMCID: PMC2909079.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"cardiovascular-diabetology-endocrinology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Cardiovascular Diabetology – Endocrinology Reports","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"Unsupported Journal","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Diabesity, Pulmonary venous hypertension, Pulmonary congestion, Diastolic dysfunction, Incretin-based Therapy, GLP-1 receptor agonist, GIP receptor agonist","lastPublishedDoi":"10.21203/rs.3.rs-9272120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9272120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The evaluation of diastolic function in diabesity patients undergoing incretin-based therapy could facilitate early detection and help prevent progression to heart failure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A group of 15 diabesity cases with chest x-ray examinations (\u0026lt; 1 month) before and after ≥ 12 months of incretin-based therapy (absent ventricular/valvular dysfunction) were identified. Standard diabesity descriptors and predictions of pulmonary venous hypertension (aka “pulmonary congestion”) [None; Stage 1: vascular distention/redistribution but minimal interstitial edema; or Stage ≥ 2: vascular congestion with ≥ mild interstitial or alveolar edema] by validated AI-enabled chest x-ray staging, representing mean left atrial pressure in diastolic dysfunction, were evaluated pre- and post-therapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy weight loss, cases clustered into equal-sized subgroups: 1. Significant Responders (9–30% decreases); 2. Insignificant Responders (0–2% decreases); and 3. Non-Responders (2–12% increases). Regarding hemoglobin A1c changes: 1. Significant Responders collectively decreased; 2. Insignificant Responders varied; and 3. Non-Responders were largely stable. Pre-therapy, all 15 cases demonstrated AI-enabled chest x-ray evidence of diastolic dysfunction; post-therapy, 4 improved (especially cases of greatest weight loss or hemoglobin A1c reduction), 5 were stable, and 6 worsened. Per subgroup, AI-enabled chest x-ray indications therapeutic response were: 1. Significant Responders (all demonstrating unequivocally decreased obesity and improved diabetes) collectively showed stable-decreased dysfunction; 2. Insignificant Responders (all demonstrating minimally decreased obesity but stable-worsening diabetes in most) reflected stable-increased dysfunction in 60%; and 3. Non-Responders (all demonstrating increased obesity but stable-improved diabetes) showed increased dysfunction in 80%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI-enabled CXR staging detects background subclinical diastolic dysfunction in diabesity and monitors its response to incretin-based therapy.\u003c/p\u003e","manuscriptTitle":"AI-Enabled Chest X-Ray Detects Subclinical Diastolic Dysfunction in Diabesity and its Therapeutic Responses to GLP-1 or GLP-1/GIP Receptor Agonists","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-05 17:13:16","doi":"10.21203/rs.3.rs-9272120/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T12:54:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T11:48:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36425673359965998670939464387717664182","date":"2026-04-11T09:58:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T13:48:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231148715509038237733463518692035854763","date":"2026-04-10T05:40:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80277551406078931445435920177495577681","date":"2026-03-31T16:39:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T12:31:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T12:28:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T11:35:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology – Endocrinology Reports","date":"2026-03-30T22:04:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"cardiovascular-diabetology-endocrinology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Cardiovascular Diabetology – Endocrinology Reports","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"Unsupported Journal","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"49e053a2-26d9-4253-b5c9-09aa708ded1d","owner":[],"postedDate":"April 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-02T18:38:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-05 17:13:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9272120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9272120","identity":"rs-9272120","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}